Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories
文献类型：SCI/SSCI论文
| 作者 | Bai H. X.; Li, D. Y.; Ge, Y.; Wang, J. F. |
| 发表日期 | 2016 |
| 关键词 | Spatial associations Nominal variable Conditional probability distribution Adjacency matrix neural-tube defects local indicators birth-defects risk-factors morans i autocorrelation models images land statistics |
| 英文摘要 | How to automatically mining the spatial association patterns in spatial data is a challenging task in spatial data mining. In this paper, we propose three indices that represent the per-class, inter-class, and overall spatial associations of a nominal variable, which are based on the conditional probabilities of surface object categories. These indices represent relative quantities and are normalized to the region [-1, 1], which more accord with the intuitive cognition of people. We present some algorithms for detecting spatial associations that are based on these indices. The proposed method can be regarded as an extension of join count statistics and Transiogram. Several constructive examples were used to illustrate the advantages of the new method. Using two real data sets, vegetation types in Qingxian, Shanxi, China and neural tube birth defects in Heshun, Shanxi, China, we ran comparative experiments with other commonly used methods, including join count statistics, co-location quotient, and Q(m) statistics. The experimental results show that the proposed method can detect more subtle spatial associations, and is not sensitive to the sequence of neighbors. (C) 2015 Elsevier Inc. All rights reserved. |
| 出处 | Information Sciences |
| 卷 | 329 |
| 页 | 701-718 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0020-0255 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/43375]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Bai H. X.,Li, D. Y.,Ge, Y.,et al. Detecting nominal variables' spatial associations using conditional probabilities of neighboring surface objects' categories. 2016. |
入库方式： OAI收割
来源：地理科学与资源研究所
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